Title: Statistical Methods for Identifying Differentially Expressed Genes in Replicated cDNA Microarray Exp
1Statistical Methods for Identifying
Differentially Expressed Genes in Replicated cDNA
Microarray Experiments
- Presented by Nan Lin
- 13 October 2002
2Introduction to cDNA Microarray Experiment
- Single-slide Design
- Two mRNA samples (red/green) on the same slide
- Multiple-slide Design
- Two or more types of mRNA on different slides
- Exclude time-course experiment
3Examples of Multiple-slide Design
- Apo AI
- Treatment group 8 mice with apo AI gene knocked
out - Control group 8 C57B1/6 mice
- Cy5 each of 16 mice
- Cy3 pooling cDNA from 8 control mice
- SR-BI
- Treatment group 8 SR-BI transgenic mice
- Control group 8 normal FVB mice
- Microarray Setup
- 6384 spots, 4X4 grids with 19X21 spots in each
4Single-slide Methods
- Two types
- Based solely on intensity ratio R/G
- Take into account overall transcript abundance
measured by RG - Historical Review
- Fold increase/decrease cut-offs (1995-1996)
- Probabilistic modeling based on distributional
assumptions (1997-2000) - Consider RG (2000-2001) e.g. Gamma-Gamma-Bernoull
i
5Summary of Single-slide Methods
- Producing a model dependent rule drawing two
curves in the (R,G) plane - Power (1-Type II error rate)
- False positive rate (Type I error rate)
- Multiple testing
- Replication is needed because gene expression
data are too noisy
6Image Analysis
- Raw data 16-bit TIFF files
- Addressing
- Within a batch, important characteristics are
similar - Segmentation
- Seeded region growing algorithm
- Background adjustment
- Morphological opening (a nonlinear filter)
- Software package Spot in R environment
7Single-slide Data Display
- Plot log2R vs. log2G
- variation less dependent on absolute magnitude
- normalization is additive for logged intensities
- evens out highly skewed distributions
- a more realistic sense of variation
- Plot Mlog2 (R/G) vs. Alog2(RG)/2
- More revealing in terms of identifying spot
artifacts and for normalization purpose
8Normalization
- Identify and remove sources of systematic
variation other than differential expression - Different labeling efficiencies and scanning
properties for Cy3 and Cy5 - Different scanning parameters
- Print-tip, spatial or plate effects
- Red intensity is often lower than green intensity
- The imbalance between R and G varies
- across spots and between arrays
- Overall spot intensity A
- Location on the array, plate origin, etc.
9An Example Self-Self Experiment
10Normalization (Cont.)
- Global normalization
- subtract mean or median from all intensity
log-ratios - More complex normalization
- Robust locally weighted regression
- Mspot intensity Alocationplate origin
- Use print-tip group to represent the spot
locations - log2 (R/G)? log2 (R/G) l(A,j)
- l(A,j) lowess in R (0.2ltflt0.4)
- Control sequences
11Apo AI Normalization
12Graphical Display for Test Statistics (I)
- Test statistics
- Hj no association between treatment and the
expression level of gene j, j1,,m. - Two-sided alternative
- Two-sample Welch t-statistics
- Replication is essential to assess the
variability in treatment and control group - The joint distribution is estimated by a
permutation procedure because the actual
distribution is not a t-distribution
13Graphical Display for Test Statistics (II)
14Graphical Display for Test Statistics (III)
- Plots vs. absolute expression levels
15Multiple Hypothesis Testing Adjusted p-values
(I)
- P-value PjPr(TjgttjHj), j1,,m.
- Family-wise Type I Error Rate (FWER)
- The probability of at least one Type I error in
the family - Strong Control of the FWER
- Control the FWER for any combination of true and
false hypotheses - Weak Control of the FWER
- Control the FWER only under the complete null
hypothesis that all hypotheses in the family are
true
16Multiple Hypothesis Testing Adjusted p-values
(II)
- Adjusted p-value for Hj
- Pjinfa Hj is rejected at FWERa
- Hj is rejected at FWER a if Pjlta
- P-value adjustment approaches
- Bonferroni
- Sidak single-step
- Holm step-down
- Westfall and Young step-down minP
17Multiple Hypothesis Testing Estimation of
adjusted p-values (I)
18Multiple Hypothesis Testing Estimation of
adjusted p-values (II)
19Apo AI Adjusted p-values (I)
20Apo AI Adjusted p-values (II)
21Apo AI Comparison with Single-slide Methods
22Discussion
- M-A plots
- Normalization
- Robust local regression, e.g. lowess
- Q-Q plots Plots vs. absolute expression level
- False discovery rate (FDR)
- Replication is necessary
- Design issues
- Factorial experiments
- Joint behavior of genes
- R package SMA